@@ -28,7 +28,7 @@ From the perspective of application program, you need to consider:
- Writing to known existing tables is more efficient than writing to uncertain tables in automatic creating mode because the later needs to check whether the table exists or not before actually writing data into it.
- Writing in SQL is more efficient than writing in schemaless mode because schemaless writing creates table automatically and may alter table schema.
Application programs need to take care of the above factors and try to take advantage of them. The application progam should write to single table in each write batch. The batch size needs to be tuned to a proper value on a specific system. The number of concurrent connections needs to be tuned to a proper value too to achieve the best writing throughput.
Application programs need to take care of the above factors and try to take advantage of them. The application program should write to single table in each write batch. The batch size needs to be tuned to a proper value on a specific system. The number of concurrent connections needs to be tuned to a proper value too to achieve the best writing throughput.
// aggregate start function. The intermediate value or the state(@interBuf) is initialized in this function. The function name shall be concatenation of udf name and _start suffix
// @param interbuf intermediate value to intialize
// @param interbuf intermediate value to initialize
// @return error number defined in taoserror.h
int32_taggfn_start(SUdfInterBuf*interBuf){
// initialize intermediate value in interBuf
returnTSDB_CODE_SUCESS;
returnTSDB_CODE_SUCCESS;
}
// aggregate reduce function. This function aggregate old state(@interbuf) and one data bock(inputBlock) and output a new state(@newInterBuf).
The preceding SQL statement shows all supertables in the current TDengine database, including the name, creation time, number of columns, number of tags, and number of subtabels for each supertable.
The preceding SQL statement shows all supertables in the current TDengine database, including the name, creation time, number of columns, number of tags, and number of subtables for each supertable.
@@ -248,7 +248,7 @@ You can also use the NULLS keyword to specify the position of null values. Ascen
The LIMIT keyword controls the number of results that are displayed. You can also use the OFFSET keyword to specify the result to display first. `LIMIT` and `OFFSET` are executed after `ORDER BY` in the query execution. You can include an offset in a LIMIT clause. For example, LIMIT 5 OFFSET 2 can also be written LIMIT 2, 5. Both of these clauses display the third through the seventh results.
In a statement that includes a PARTITON BY clause, the LIMIT keyword is performed on each partition, not on the entire set of results.
In a statement that includes a PARTITION BY clause, the LIMIT keyword is performed on each partition, not on the entire set of results.
@@ -666,13 +666,13 @@ If you input a specific column, the number of non-null values in the column is r
ELAPSED(ts_primary_key[,time_unit])
```
**Description**：`elapsed` function can be used to calculate the continuous time length in which there is valid data. If it's used with `INTERVAL` clause, the returned result is the calcualted time length within each time window. If it's used without `INTERVAL` caluse, the returned result is the calculated time length within the specified time range. Please be noted that the return value of `elapsed` is the number of `time_unit` in the calculated time length.
**Description**：`elapsed` function can be used to calculate the continuous time length in which there is valid data. If it's used with `INTERVAL` clause, the returned result is the calculated time length within each time window. If it's used without `INTERVAL` caluse, the returned result is the calculated time length within the specified time range. Please be noted that the return value of `elapsed` is the number of `time_unit` in the calculated time length.
**Return value type**: Double if the input value is not NULL;
**Return value type**: TIMESTAMP
**Applicable tables**: table, STable, outter in nested query
**Applicable tables**: table, STable, outer in nested query
**Explanations**：
-`ts_primary_key` parameter can only be the first column of a table, i.e. timestamp primary key.
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@@ -754,7 +754,7 @@ HYPERLOGLOG(expr)
**Description**：
The cardinal number of a specific column is returned by using hyperloglog algorithm. The benefit of using hyperloglog algorithm is that the memory usage is under control when the data volume is huge.
However, when the data volume is very small, the result may be not accurate, it's recommented to use `select count(data) from (select unique(col) as data from table)` in this case.
However, when the data volume is very small, the result may be not accurate, it's recommended to use `select count(data) from (select unique(col) as data from table)` in this case.
**Return value type**: Integer
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@@ -801,7 +801,7 @@ PERCENTILE(expr, p [, p1] ...)
**Description**: The value whose rank in a specific column matches the specified percentage. If such a value matching the specified percentage doesn't exist in the column, an interpolation value will be returned.
**Return value type**: This function takes 2 minumum and 11 maximum parameters, and it can simultaneously return 10 percentiles at most. If 2 parameters are given, a single percentile is returned and the value type is DOUBLE.
**Return value type**: This function takes 2 minimum and 11 maximum parameters, and it can simultaneously return 10 percentiles at most. If 2 parameters are given, a single percentile is returned and the value type is DOUBLE.
If more than 2 parameters are given, the return value type is a VARCHAR string, the format of which is a JSON ARRAY containing all return values.
**Applicable column types**: Numeric
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@@ -811,7 +811,7 @@ PERCENTILE(expr, p [, p1] ...)
**More explanations**:
- _p_ is in range [0,100], when _p_ is 0, the result is same as using function MIN; when _p_ is 100, the result is same as function MAX.
- When calculating multiple percentiles of a specific column, a single PERCENTILE function with multiple parameters is adviced, as this can largely reduce the query response time.
- When calculating multiple percentiles of a specific column, a single PERCENTILE function with multiple parameters is advised, as this can largely reduce the query response time.
For example, using SELECT percentile(col, 90, 95, 99) FROM table will perform better than SELECT percentile(col, 90), percentile(col, 95), percentile(col, 99) from table.
## Selection Functions
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@@ -884,6 +884,15 @@ INTERP(expr)
- Pseudocolumn `_irowts` can be used along with `INTERP` to return the timestamps associated with interpolation points(support after version 3.0.2.0).
- Pseudocolumn `_isfilled` can be used along with `INTERP` to indicate whether the results are original records or data points generated by interpolation algorithm(support after version 3.0.3.0).
**Example**
- We use the smart meters example used in this documentation to illustrate how to use the INTERP function.
- We want to downsample every 1 hour and use a linear fill for missing values. Note the order in which the "partition by" clause and the "range", "every" and "fill" parameters are used.
```sql
SELECT _irowts,INTERP(current) FROM test.meters PARTITION BY TBNAME RANGE('2017-07-22 00:00:00','2017-07-24 12:25:00') EVERY(1h) FILL(LINEAR)
@@ -21,7 +21,7 @@ part_list can be any scalar expression, such as a column, constant, scalar funct
A PARTITION BY clause is processed as follows:
- The PARTITION BY clause must occur after the WHERE clause
- The PARTITION BY caluse partitions the data according to the specified dimentions, then perform computation on each partition. The performed computation is determined by the rest of the statement - a window clause, GROUP BY clause, or SELECT clause.
- The PARTITION BY caluse partitions the data according to the specified dimensions, then perform computation on each partition. The performed computation is determined by the rest of the statement - a window clause, GROUP BY clause, or SELECT clause.
- The PARTITION BY clause can be used together with a window clause or GROUP BY clause. In this case, the window or GROUP BY clause takes effect on every partition. For example, the following statement partitions the table by the location tag, performs downsampling over a 10 minute window, and returns the maximum value:
```sql
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@@ -105,7 +105,7 @@ SELECT COUNT(*) FROM temp_tb_1 INTERVAL(1m) SLIDING(2m);
When using time windows, note the following:
- The window length for aggregation depends on the value of INTERVAL. The minimum interval is 10 ms. You can configure a window as an offset from UTC 0:00. The offset cannot be smaler than the interval. You can use SLIDING to specify the length of time that the window moves forward.
- The window length for aggregation depends on the value of INTERVAL. The minimum interval is 10 ms. You can configure a window as an offset from UTC 0:00. The offset cannot be smaller than the interval. You can use SLIDING to specify the length of time that the window moves forward.
Please note that the `timezone` parameter should be configured to be the same value in the `taos.cfg` configuration file on client side and server side.
- The result set is in ascending order of timestamp when you aggregate by time window.
| 3 | sql | BINARY(1024) | SQL statement used to create the stream |
| 4 | status | BIANRY(20) | Current status |
| 4 | status | BINARY(20) | Current status |
| 5 | source_db | BINARY(64) | Source database |
| 6 | target_db | BIANRY(64) | Target database |
| 6 | target_db | BINARY(64) | Target database |
| 7 | target_table | BINARY(192) | Target table |
| 8 | watermark | BIGINT | Watermark (see stream processing documentation). It should be noted that `watermark` is a TDengine keyword and needs to be escaped with ` when used as a column name. |
| 9 | trigger | INT | Method of triggering the result push (see stream processing documentation). It should be noted that `trigger` is a TDengine keyword and needs to be escaped with ` when used as a column name. |
description:This document describes how to use the SHOW statement in TDengine.
---
`SHOW` command can be used to get brief system information. To get details about metatadata, information, and status in the system, please use `select` to query the tables in database `INFORMATION_SCHEMA`.
`SHOW` command can be used to get brief system information. To get details about metadata, information, and status in the system, please use `select` to query the tables in database `INFORMATION_SCHEMA`.
@@ -300,7 +300,7 @@ stmt.executeUpdate("create table if not exists tb (ts timestamp, temperature int
> **Note**: If you do not use `use db` to specify the database, all subsequent operations on the table need to add the database name as a prefix, such as db.tb.
@@ -39,7 +39,7 @@ The Rust Connector is still under rapid development and is not guaranteed to be
* Install the Rust development toolchain
* If using the native connection, please install the TDengine client driver. Please refer to [install client driver](/reference/connector#install-client-driver)
# Add taos dependency
### Add taos dependency
Depending on the connection method, add the [taos][taos] dependency in your Rust project as follows:
...
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@@ -282,7 +282,7 @@ In the application code, use `pool.get()? ` to get a connection object [Taos].
let taos = pool.get()?;
```
# Connectors
### Connectors
The [Taos][struct.Taos] object provides an API to perform operations on multiple databases.
@@ -208,7 +208,7 @@ taosBenchmark -A INT,DOUBLE,NCHAR,BINARY\(16\)
Keep trying if failed to insert, default is no. Available with v3.0.9+.
-**-z/--trying-interval <NUMBER\>** :
Specify interval between keep trying insert. Valid value is a postive number. Only valid when keep trying be enabled. Available with v3.0.9+.
Specify interval between keep trying insert. Valid value is a positive number. Only valid when keep trying be enabled. Available with v3.0.9+.
-**-V/--version** :
Show version information only. Users should not use it with other parameters.
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...
@@ -239,7 +239,7 @@ The parameters listed in this section apply to all function modes.
-** keep_trying ** : Keep trying if failed to insert, default is no. Available with v3.0.9+.
-** trying_interval ** : Specify interval between keep trying insert. Valid value is a postive number. Only valid when keep trying be enabled. Available with v3.0.9+.
-** trying_interval ** : Specify interval between keep trying insert. Valid value is a positive number. Only valid when keep trying be enabled. Available with v3.0.9+.
#### Database related configuration parameters
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@@ -352,7 +352,7 @@ The configuration parameters for specifying super table tag columns and data col
-**min**: The minimum value of the column/label of the data type. The generated value will equal or large than the minimum value.
-**max**: The maximum value of the column/label of the data type. The generated value will less than the maxium value.
-**max**: The maximum value of the column/label of the data type. The generated value will less than the maximum value.
-**values**: The value field of the nchar/binary column/label, which will be chosen randomly from the values.
@@ -24,7 +24,7 @@ All executable files of TDengine are in the _/usr/local/taos/bin_ directory by d
- _taosdump_: data import and export tool
- _taosBenchmark_: TDengine testing tool
- _remove.sh_: script to uninstall TDengine, please execute it carefully, link to the **rmtaos** command in the /usr/bin directory. Will remove the TDengine installation directory `/usr/local/taos`, but will keep `/etc/taos`, `/var/lib/taos`, `/var/log/taos`
- _taosadapter_: server-side executable that provides RESTful services and accepts writing requests from a variety of other softwares
- _taosadapter_: server-side executable that provides RESTful services and accepts writing requests from a variety of other software
- _TDinsight.sh_: script to download TDinsight and install it
- _set_core.sh_: script for setting up the system to generate core dump files for easy debugging
- _taosd-dump-cfg.gdb_: script to facilitate debugging of taosd's gdb execution.
@@ -131,7 +131,7 @@ create stable st (_ts timestamp, c1 bigint, c2 bool, c3 binary(6), c4 bigint) ta
This section describes the impact on the schema caused by different data being written.
If you use line protocol to write to a specific tag field and then later change the field type, a schema error will ocur. This triggers an error on the write API. This is shown as follows:
If you use line protocol to write to a specific tag field and then later change the field type, a schema error will occur. This triggers an error on the write API. This is shown as follows:
@@ -31,7 +31,7 @@ The default database name written by taosAdapter is `statsd`. To specify a diffe
### Configuring StatsD
To use StatsD, you need to download its [source code](https://github.com/statsd/statsd). Please refer to the example file `exampleConfig.js` in the root directory of the source download to modify the configuration file. In <taosAdpater's host\>, please fill in the domain name or IP address of the server running taosAdapter, and <port for StatsD\>, please fill in the port where taosAdapter receives StatsD data (default is 6044).
To use StatsD, you need to download its [source code](https://github.com/statsd/statsd). Please refer to the example file `exampleConfig.js` in the root directory of the source download to modify the configuration file. In <taosAdapter's host\>, please fill in the domain name or IP address of the server running taosAdapter, and <port for StatsD\>, please fill in the port where taosAdapter receives StatsD data (default is 6044).
The end point and role/status (leader, follower, candidate, offline) of all mnodes can be shown by the above command. When the first dnode is started in a cluster, there must be one mnode in this dnode. Without at least one mnode, the cluster cannot work.
From TDengine 3.0.0, RAFT procotol is used to guarantee the high availability, so the number of mnodes is should be 1 or 3.
From TDengine 3.0.0, RAFT protocol is used to guarantee the high availability, so the number of mnodes is should be 1 or 3.
The proper value of `vgroups` depends on available system resources. Assuming there is only one database to be created in the system, then the number of `vgroups` is determined by the available resources from all dnodes. In principle more vgroups can be created if you have more CPU and memory. Disk I/O is another important factor to consider. Once the bottleneck shows on disk I/O, more vgroups may downgrad the system performance significantly. If multiple databases are to be created in the system, then the total number of `vroups` of all the databases are dependent on the available system resources. It needs to be careful to distribute vgroups among these databases, you need to consider the number of tables, data writing frequency, size of each data row for all these databases. A recommended practice is to firstly choose a starting number for `vgroups`, for example double of the number of CPU cores, then try to adjust and optimize system configurations to find the best setting for `vgroups`, then distribute these vgroups among databases.
Furthermode, TDengine distributes the vgroups of each database equally among all dnodes. In case of replica 3, the distrubtion is even more complex, TDengine tries its best to prevent any dnode from becoming a bottleneck.
Furthermode, TDengine distributes the vgroups of each database equally among all dnodes. In case of replica 3, the distribution is even more complex, TDengine tries its best to prevent any dnode from becoming a bottleneck.
TDegnine utilizes the above ways to achieve load balance in a cluster, and finally achieve higher throughput.
Once the load balance is achieved, after some operations like deleting tables or droping databases, the load across all dnodes may become inbalanced, the method of rebalance will be provided in later versions. However, even without explicit rebalancing, TDengine will try its best to achieve new balance without manual interfering when a new database is created.
\ No newline at end of file
Once the load balance is achieved, after some operations like deleting tables or dropping databases, the load across all dnodes may become imbalanced, the method of rebalance will be provided in later versions. However, even without explicit rebalancing, TDengine will try its best to achieve new balance without manual interfering when a new database is created.
Log in to the Grafana interface using a web browser at `IP:3000`, with the system's initial username and password being `admin/admin`.
Click on the gear icon on the left and select `Plugins`, you should find the TDengine data source plugin icon.
Click on the plus icon on the left and select `Import` to get the data from `https://github.com/taosdata/grafanaplugin/blob/master/examples/telegraf/grafana/dashboards/telegraf-dashboard-v3.json` (for Tdengine 3.0. for TDengine 2.x, please use `telegraf-dashboard-v2.json`), download the dashboard JSON file and import it. You will then see the dashboard in the following screen.
Click on the plus icon on the left and select `Import` to get the data from `https://github.com/taosdata/grafanaplugin/blob/master/examples/telegraf/grafana/dashboards/telegraf-dashboard-v3.json` (for TDengine 3.0. for TDengine 2.x, please use `telegraf-dashboard-v2.json`), download the dashboard JSON file and import it. You will then see the dashboard in the following screen.
// aggregate start function. The intermediate value or the state(@interBuf) is initialized in this function. The function name shall be concatenation of udf name and _start suffix
// @param interbuf intermediate value to intialize
// @param interbuf intermediate value to initialize
// @return error number defined in taoserror.h
int32_taggfn_start(SUdfInterBuf*interBuf){
// initialize intermediate value in interBuf
returnTSDB_CODE_SUCESS;
returnTSDB_CODE_SUCCESS;
}
// aggregate reduce function. This function aggregate old state(@interbuf) and one data bock(inputBlock) and output a new state(@newInterBuf).
